Agentic AI
AI systems that pursue a goal by planning, using tools and taking multi-step actions with some autonomy, rather than only answering a single prompt.
Agentic AI describes systems built to pursue a goal rather than answer one question. An agent breaks a goal into steps, chooses tools, takes actions in other software, observes the results and adjusts, with varying levels of autonomy and human oversight.
A chatbot answers. An agent does: it searches, reads files, calls APIs, edits documents, sends messages or updates a system of record, often over many steps.
What changes with agents
- Interfaces. Agents do not need your application's screens; they need its capabilities. This puts a premium on capability readiness, and on standards such as the Model Context Protocol.
- Authority. Because an agent can act, the line between what it can do and what it may do matters: capability is not authority.
- Evaluation. The harness around the model shapes performance, so evaluation has to cover the whole system.
- Time. Agents that carry goals across days raise the stakes of persistent delegation and context debt.
Agentic transformation is therefore as much about operating models, supervision and recovery as about the model.
Read more in The next AI interface may never be seen.
Related terms
Harness
The software around a model that manages context, tool use, sub-agents and the execution environment. It shapes real-world capability, so it must be evaluated with the model, not ignored.
Persistent delegation
Handing an AI agent a goal it keeps carrying over time — remembering context, noticing relevant events and acting across applications — rather than completing a single prompted task.
Capability is not authority
A design principle for AI agents: being technically able to perform an action does not mean the agent should be permitted to perform it. Delegation needs gradients of authority.
Model Context Protocol
An open standard, usually called MCP, for connecting AI models and agents to external tools and data sources in a consistent way, so they can discover and call an organization's capabilities.
Used in these essays
The biggest AI mistake companies make is starting with AI
An AI-first mandate can turn discovery into justification. The missing discipline is preserving the right to choose a better, simpler answer.
Your AI program can be efficient and still be strategically irrelevant
AI can make low-value work dramatically cheaper. That is still low-value work. The portfolio question is not only what became faster, but whether the work deserved investment in the first place.
AI is removing the coordination tax — the next bottleneck is authority
As AI reduces the cost of moving work between teams and systems, many organizations will discover that the remaining delay is not coordination. It is permission to decide.
Your AI agent estate is becoming a capital allocation problem
Once agents multiply across workflows, the management problem changes: which agents deserve more capacity, which should be merged or constrained, and which should be retired?
An AI agent should get authority per task, not permissions per role
A person's role describes standing access across many situations. An AI agent should receive a narrower authority envelope for the task it is executing now.
Design systems need an interaction policy layer for AI
Components can keep an AI experience visually on-system while the agent is behaviorally off-policy. Design systems now need reusable rules for what AI may present, decide, execute, defer, expose as uncertain, confirm, reverse and escalate.
Your AI agent can follow the objective and still break the company
AI agents can satisfy a local objective while violating the organization’s broader constraints. The control problem is not only model alignment; it is management design.
Stop adopting AI
AI adoption is rising faster than enterprise value because companies keep installing new intelligence inside old operating models.
OpenAI Decisions API turns probability into policy
The provocative part of OpenAI's Decisions API is not that AI can make choices. It is that a model score can quietly become an action — and an action can quietly become policy.
OpenAI Dots is a test of whether AI can carry a goal, not just complete a task
Dots matters less as another capable assistant than as a test of persistent delegation: can AI keep carrying a goal without giving the user a new system to manage?
The wrong questions about AI right now
Many of the questions that helped us orient ourselves around generative AI are now too blunt to be useful. The harder work is no longer asking what AI is in the abstract, but specifying where it works, where it fails, what authority it should have, and what the whole system costs.
A cheaper AI model can move the cost instead of removing it
A lower model bill can hide a higher workflow bill. The useful AI TCO question is not only what got cheaper, but where the cost moved.
Make the AI vendor demo fail
A polished AI demo proves that a system can succeed under prepared conditions. A buying decision needs different evidence: what happens when the system is wrong, blocked, uncertain or halfway through an action.
The hidden metric in AI automation is supervision
As AI moves from assisting work to leading it, hours saved stop telling the whole story. The scarce resource shifts to human supervision: approvals, exceptions, context and judgment.
The next AI interface may never be seen
Agents are turning software capabilities into an interface of their own. The next enterprise design problem is deciding what should be callable, by whom, and under which boundaries.
The agent is not the operating model
Giving AI more autonomy does not remove organizational complexity. It gives that complexity permission to act.
Your chatbot is borrowing from the next interaction
AI customer service is usually measured one interaction at a time. But a failed automated interaction can change which channel a customer chooses next time. That makes future adoption part of the economics, not a separate trust metric.